Guiding Antibiotic Therapy with Machine Learning: Real-World Applications of a CDSS in Bacteremia Management

Background: Bacteremia is a life-threatening condition contributing significantly to sepsis-related mortality worldwide. With delayed appropriate antibiotic therapy, mortality increases by 20% regardless of antimicrobial resistance. This study evaluated the perceived clinical utility of Artificial Intelligence (AI)-powered Clinical Decision Support Systems (CDSS) (OneChoice and OneChoice Fusion) among specialist physicians managing bacteremia cases. Methods: A cross-sectional survey was conducted with 65 physicians from multiple medical specialties who were presented with clinical vignettes describing patients with bacteremia and corresponding 90 AI-CDSS recommendations. Participants assessed perceived helpfulness, the impact of AI decision-making, concordance between AI recommendations and their own clinical judgment, and whether changing therapy based on CDSS recommendations is valid. Results: The study encompassed a diverse range of bacterial pathogens, with Escherichia coli representing 38.7% of the isolates and 30% being extended-spectrum β-lactamase (ESBL) producers. Results demonstrated 97.8% of physicians reported that AI facilitated decision-making and substantial concordance (87.8%) between AI recommendations and physicians’ therapeutic recommendations. Implementation analysis revealed a meaningful clinical impact, with 68.9% of cases resulting in AI-guided treatment modifications. Conclusions: These findings indicate that AI-powered CDSS effectively bridge critical gaps in infectious disease expertise and antimicrobial stewardship. Future research should prioritize prospective clinical trials evaluating direct patient outcomes to establish evidence of broader clinical effectiveness and application across diverse healthcare settings.